About this course: This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
Taught by: Mine Çetinkaya-Rundel, Assistant Professor of the Practice
Department of Statistical Science
Taught by: David Banks, Professor of the Practice
Statistical Science
Taught by: Colin Rundel , Assistant Professor of the Practice
Statistical Science
Taught by: Merlise A Clyde, Professor
Department of Statistical Science
| Basic Info | Course 4 of 5 in the Statistics with R Specialization |
| Level | Intermediate |
| Commitment | 5 weeks of study, 5-7 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Introduction to Statistics with R
- Reading: About Statistics with R Specialization
- Reading: About Bayesian Statistics
- Reading: Special Thanks
- Reading: Weekly Feedback Forms
- Discussion Prompt: Introduce Yourself
- Video: The Basics of Bayesian Statistics
- Reading: Module Learning Objectives
- Video: Conditional Probabilities and Bayes' Rule
- Video: Bayes' Rule and Diagnostic Testing
- Video: Bayes Updating
- Video: Bayesian vs. frequentist definitions of probability
- Video: Inference for a Proportion: Frequentist Approach
- Video: Inference for a Proportion: Bayesian Approach
- Video: Effect of Sample Size on the Posterior
- Video: Frequentist vs. Bayesian Inference
- Practice Quiz: Week 1 Practice Quiz
- Reading: Week 1 Lab Instructions
- Reading: Week 1 Feedback Form
- Video: Bayesian Inference
- Reading: Module Learning Objectives
- Video: From the Discrete to the Continuous
- Video: Elicitation
- Video: Conjugacy
- Video: Inference on a Binomial Proportion
- Video: The Gamma-Poisson Conjugate Families
- Video: The Normal-Normal Conjugate Families
- Video: Non-Conjugate Priors
- Video: Credible Intervals
- Video: Predictive Inference
- Practice Quiz: Week 2 Practice Quiz
- Reading: Week 2 Lab Instructions
- Reading: Weekly Feedback Form
- Video: Decision making
- Reading: Module Learning Objectives
- Video: Losses and decision making
- Video: Working with loss functions
- Video: Minimizing expected loss for hypothesis testing
- Video: Posterior probabilities of hypotheses and Bayes factors
- Video: Comparing two proportions using Bayes factors: assumptions
- Video: Comparing two proportions using Bayes factors
- Video: Comparing two paired means using Bayes factors
- Video: What to report?
- Video: Posterior probability, p-values and paradoxes
- Video: Comparing two independent means
- Video: Comparing two independent means: hypothesis testing
- Practice Quiz: Week 3 Practice Quiz
- Reading: Week 3 Lab Instructions
- Reading: Weekly Feedback Form
- Video: Bayesian regression
- Reading: Module Learning Objectives
- Video: Bayesian simple linear regression
- Video: Checking for outliers
- Video: Bayesian multiple regression
- Video: Model selection criteria
- Video: Bayesian model uncertainty
- Video: Bayesian model averaging
- Video: Stochastic exploration
- Video: Priors for Bayesian model uncertainty
- Video: R demo: crime and punishment
- Video: Decisions under model uncertainty
- Practice Quiz: Week 4 Practice Quiz
- Reading: Week 4 Lab Instructions
- Reading: Week 4 Lab Supplement
- Reading: Weekly Feedback Form
- Video: Perspectives on Bayesian applications
- Video: Bayesian inference: a talk with Jim Berger
- Video: Bayesian methods and big data: a talk with David Dunson
- Video: Bayesian methods in biostatistics and public health: a talk with Amy Herring
- Video: Bayes in industry: a talk with Steve Scott
- Reading: Weekly Feedback Form
- Reading: Project information
- Reading: Weekly Feedback Form
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|---|---|---|
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| Access to graded materials | Not available | Available |
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Good
This is one of many good courses that one can get a glimpse of Bayesian statistics though it lacks of thorough explanation of mathematical background and reading materials of any kind.
It was nice learning all the distribution functions and Bayesian statistics. However, I have one suggestion: When going through equations, it's better to dive a little deeper into them, or at least go through a few steps of derivation, rather than just show them on the screen. For example, in 'Bayesian Regression' when introducing 'conjugate bivariant normal-gamma distribution, it was directly given three correlations on the screen: (1) alpha | sigma^2 ~ N(a0, sigma^2 S_alpha, (2) beta | sigma^2 ~ N(b0, sigma^2 S_beta), (3) 1/(sigma^2) ~ G(mu_0/2, mu_0 sigma^2/2. There are many terms in the equation. It would be more learner friendly if one can at least go through what term corresponding to what. Or if time is a constraint one can at least show some reasonable reference, so that learners can search for papers. I had to do quite an amount of googling to get through these things.
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This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.